Fast foveation camera and controlling algorithms

ABSTRACT

Provided is a camera system to obtain high resolution imagery on multiple regions of interest even when the regions of interest are ate different focal depths and in different viewing directions. Embodiments include a high speed camera that operates by reflecting a beam of interest corresponding to a scene of interest into a high-speed passive sensor from a dynamic optical modulator. Embodiments described herein provide a foveating camera design that distributes resolution onto regions of interest by imaging reflections off a scanning micro-electromechanical system (MEMS) mirror. MEMS mirrors are used herein to modulate viewing direction. Embodiments include a camera capturing reflections off of a tiny, fast moving mirror.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationSer. No. 62/835,864, filed on Apr. 18, 2019, the contents of which arehereby incorporated by reference in their entirety.

GOVERNMENT SUPPORT

This invention was made with government support under Grant 1514154awarded by the National Science Foundation and under GrantN00014-18-1-2663 awarded by the U.S. Navy Office of Naval Research. Thegovernment has certain rights in the invention.

TECHNICAL FIELD

The present application relates to the technical field of highmodulation speed, variable angular resolution cameras. In particular,the invention relates to high speed cameras/imaging sensors with fastmovements. These can be, but are not limited to, real-time artificialsaccadic movements. In addition, the invention allows for controlalgorithms that take the previous or current measurements and determinesthe optimal next measurements to be made to enable some sensing orimaging task.

BACKGROUND

Existing camera technologies suffer from an inability to offer variableangular resolution in combination with high speed modulation. As anexample, FIG. 1 depicts a diagram of various existing cameras arrangedaccording to their relative abilities with respect to variable angularresolution and high speed modulation. A limiting factor of existingcameras is their reliance on optomechanical components as indicated byline 5. The components fundamentally limit the speed with which existingcameras can “zoom” or otherwise adjust.

As a result, existing cameras are limited in their ability to identifywhat is visually important in a given scene and allocate visualattention to these important regions. There is therefore a need in theart for a camera that can offer artificial saccades via high speedfoveation. Despite decades of saliency research in the human vision andcomputer vision communities, no strategy for learned models exist toextract scene importance for a set of visual tasks, at each timeinstance, for dynamic, complex scenes.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1 is a diagram depicting the comparative capability of existingcamera technologies;

FIG. 2 is a schematic block diagram of a fast foveation camera accordingto various embodiments of the present disclosure;

FIG. 3 illustrates an example MEMS mirror, consisting of a centralmirror plate suspended by four micro-actuators according to variousembodiments of the present disclosure;

FIG. 4 illustrates virtual camera viewpoints based on motion of the MEMSmirror according to an example embodiment of the present disclosure;

FIG. 5 illustrates a MEMS mirror, and camera with cover glass disposedtherebetween according to an example embodiment of the presentdisclosure;

FIG. 6 provides a flowchart illustrating various processes, procedures,and/or operations for calibrating a fast foveation camera according tovarious embodiments of the present disclosure;

FIG. 7 is a schematic block diagram of an example composite cameracomprising a secondary camera and a fast foveation camera according tovarious embodiments of the present disclosure;

FIG. 8 is a schematic block diagram of another example composite cameracomprising a secondary camera and a fast foveation camera according tovarious embodiments of the present disclosure;

FIG. 9 provides a flowchart illustrating various processes, procedures,and/or operations for generating secondary images corresponding to anarea of interest of a first image suing a composite camera according tovarious embodiments of the present disclosure; and

FIG. 10 is a schematic block diagram of an example image processingcomputing entity according to various embodiments of the presentdisclosure.

DETAILED DESCRIPTION

The present disclosure more fully describes various embodiments withreference to the accompanying drawings. It should be understood thatsome, but not all embodiments are shown and described herein. Indeed,the embodiments may take many different forms, and accordingly thisdisclosure should not be construed as limited to the embodiments setforth herein. Rather, these embodiments are provided so that thisdisclosure will satisfy applicable legal requirements. Like numbersrefer to like elements throughout.

Various embodiments described herein relate to a high speed camera withreal-time movements. In an example embodiment, the real time movementsinclude artificial saccadic movements. According to various embodiments,the high speed camera operates by reflecting a beam of interestcorresponding to a scene of interest into a high-speed passive sensorfrom a dynamic optical modulator. In one embodiment, the passive sensoris a CCD or CMOS image sensor, and the optical modulator is a scanningmirror. In general the modulation frequency of the optical modulator is,but not limited, in the kHz range, which enables artificial saccades,even for dynamic scenes. In one embodiment, the fast modulation isprovided by a scanning microelectromechanical system (MEMS) mirror. FIG.1 illustrates a comparison between the angular resolution and the speedof modulation of a fast foveation camera of the present disclosurecompared to traditional cameras.

While conventional cameras capture images without considering scenecontent, human eyes have fast mechanical movements that control how ascene is imaged in detail by the fovea, where visual acuity is highest.This concentrates computational (i.e., neuronal) resources in placeswhere they are needed most. Embodiments described herein provide afoveating camera design that distributes resolution onto regions ofinterest by imaging reflections off a scanning micro-electromechanicalsystem (MEMS) mirror. MEMS mirrors are used herein to modulate viewingdirection. Embodiments include a camera capturing reflections off of atiny, fast moving mirror. Embodiments can obtain high resolution imageryon multiple regions of interest, even if these are at different depthsand viewing directions.

MEMS mirrors are compact, have low-power performance, and move quickly.Speed, in particular, allows the capture of near-simultaneous imagery ofdynamic scenes from different viewing directions. Further, the mirrormoves faster than the exposure rate of most cameras, removing any visualcues that the viewpoint has changed from frame to frame. Effectively,the images from a single passive camera are interleaved from multiplevirtual cameras, each corresponding to a different mirror position.

Leveraging the fast mirror speed to multiplex the viewpoint overmultiple regions-of-interest (ROI) is only possible with a fast controlstrategy to decide which parts of the scene to capture at highresolution. Provided herein is an efficient robot planning algorithm forMEMS mirror control that can be optionally integrated with a targettracker. Rather than planning slow robot motion and varying PZT on arobot's onboard cameras, the control algorithms provided herein enablequick, computationally light-weight MEMS-mirror based changes of cameraviewpoint for dynamic scenes.

Embodiments described herein demonstrate the camera's utility throughremote eye-tracking, showing that multiplexing resolution with thedisclosed embodiments results in higher fidelity eye-tracking. Remoteeye tracking is an application that highlights the advantages ofembodiments described herein for camera design and control algorithms.Human eyes are a relatively small ROI compared to faces and bodies, andeyes exhibit small and fast movements.

The present disclosure includes a novel sensor for dynamic scenes thattemporarily distributes its angular resolution over the field of viewusing reflections off of a fast MEMS mirror. Embodiments further providea model for robot control to change the MEMS mirror path for pairs oftargets. The disclosed control algorithm is based on new closed formsolutions for differential updates of the camera state. Embodimentsinclude a gaze-tracking application created by fine tuning a recenteye-tracking neural network, demonstrating that our system enablesbetter eye tracking at a three meter range compared to a high-resolutioncommercial smartphone camera at the same location with the sameresolution.

Exemplary Fast Foveation Camera

FIG. 2 shows a block diagram of a fast foveation camera 2 according toone embodiment. As shown in FIG. 2, the fast foveation camera 2 includesa MEMS mirror (and/or MEMS mirror array) and modulator assembly 10(herein “MEMS mirror 10”), a MEMS controller 20, a high speed passivesensor 30, and a deep mapping processor 40. In the illustratedembodiment, the MEMS mirror 10 functions as a dynamic optical modulator,actuated by the MEMS controller 20. Light is reflected by the MEMSmirror 10 into the high speed passive sensor 30. One example MEMS mirroris shown in FIG. 3, where a central reflective mirror plate 11 issuspended by four microactuators 12 on its four sides. Each of themicroactuators 12 can be electrically controlled and set the height ofthe mirror plate 11 side to which it is anchored. With fourmicroactuators 12 with properly tuned driving voltage signals, themirror plate 11 can be tilted (or scanned) at any angle in an angularfield of view set by the maximum tilt angle of the MEMS mirror. In anexample embodiment, each of the microactuators is made of a foldedthin-film beam.

Embodiments provided herein employ a MEMS swiveling mirror to direct thefoveating camera viewpoint. The advantages of the MEMS mirror are speedand compactness. FIG. 4 illustrates that since the MEMS tilt angle cochanges the virtual camera 4 viewpoint, multiple viewpoints may begenerated at the inherent speed of the MEMS, which may be in tens ofkilohertz (kHz).

The mirror is presumed to fill the field-of-view (FOV) of the camera asshown in FIG. 4, which results from a triangle-based scaling equation.The equation is setup by ray tracing reflection points to behind themirror, yielding the virtual camera location. The system can then besolved using thin lens equations to determine the distance an objectneeds to be from the virtual camera to fill and have focus. According tothe illustrated embodiment of FIG. 4, from simple triangles, the cameraFOV is

${\theta = {2{\tan^{- 1}\left( \frac{s}{2f} \right)}}},$

where s is the sensor's longest dimension and f is the camera focallength. Assuming a mirror tilt to the horizontal given by

$\frac{\pi}{4},$

then the full fill of the mirror requires the satisfaction of thefollowing equations, where M is the largest mirror dimension and d isthe mirror-to-camera distance along the optical axis.

$d = {\frac{M}{2}{\sin (\alpha)}{\cot \left( \frac{\theta}{2} \right)}}$

The focal lengths for the camera resolutions are selected to targetimaging human heads at distances of five to ten meters. In particular,for a M=3.0 millimeters (mm) mirror, a focal length f=35 mm lens andCMOS OV2710 1/2.7″ s=5.0 mm camera sensor, whose FOV is filled when themirror-camera distance is 35 mm. This enables a typical human head tofill the angle θ when standing 205 centimeters (cm) from the virtualcamera, allowing multiple to be in the scene at five to ten meterdistances while maintaining focus and high resolution on subjects. Theangle α of FIG. 4 may be selected to be 45 degrees so an orthogonalrelationship between the camera and the virtual camera is upheld toensure the virtual views do not see the real camera or system optics.

Using the above-described equations, a simple calibration procedure maybe employed for a user who provides sensor dimensions, camera lensproperties, and MEMS mirror size, as the model calculates the necessaryMEMS mirror-camera distance to minimize vignetting, the optimal distancefor a face to fill a desired field of view of the image, and the maximumfield of view given the tilt of the MEMS mirror. Example embodimentsprovided herein predict the distance a face needs to be from the virtualcamera to fill either the horizontal or vertical fields of view of thecamera, and the expected resolution of a face bounding box at thisdistance.

Exemplary Resolution Calibration

An example embodiment may use a 305 mm×305 mm USAF 1951 Standard Layoutchart from Applied Image Inc. to validate the resolution of the systemacross distances. To be visible in Near Infrared (NIR) Wavelengths,embodiments obtain a custom print of this standard pattern. The systemresolution may be determined by inspecting the contrast between the lastvisible and the first non-visible group of lines. The frequency of thelast group and the chart size provides the resolution. To demonstrateresolution robustness of embodiments described herein, experiments werecompared with the resolution chart for three scenarios: the mirrordescribed above, the foveating camera with no mirror in the system, anda 12 megapixel smartphone camera. While the camera of the exampleembodiment included a ⅓-inch sensor, 0.003 mm pixel size, and 35 mm lensresulting in a 1080×1920 resolution, the smartphone camera used a ⅓-inchsensor, 0.00122 mm pixel size, and 4.15 mm lens resulting in aresolution of 3024×2268. The results of the experiment demonstrated thatthe mirror resulted in a resolution loss (lower frequency) when comparedto imaging without the mirror due to blur caused by the MEMS mirrorcover glass and adding an element to the light path in general. However,the disclosed system substantially outperforms the 12 megapixelsmartphone camera. The average system resolution of the smartphone is0.00097 cycles/mm, while the average system resolution of the systemdescribed herein when imaging the mirror is 0.010 cycles/mm, and withoutthe mirror is 0.018 cycles/mm. Higher cycles/mm indicates that thesystem is able to detect higher frequencies (distinct lines) on thechart.

Exemplary Cover Glass Calibration

The above described system is tuned to near-infrared (NIR 850 nanometers(nm)) data, which is commonly used for gaze tracking and iris detectiondue to its invariance to eye-color. For such a wavelength-sensitiveapplication, further calibration is needed to deal with the cover glassthat protects the MEMS mirror from dust and other particulates. Removingthe cover glass would give an unobstructed light path, but wouldjeopardize the MEMS mirror safety. Unfortunately, the cover glassgenerates additional reflections or “ghosting”. There are two primaryreflections referred to herein as mirror- and cover-reflections.Embodiments described herein calibrate the system to be useful for gazetracking applications. While there exist techniques to computationallyremove artifacts, embodiments described herein remove the artifactsoptically to improve signal-to-noise ratio and maintain speed ofcapture.

FIG. 5 illustrates an example embodiment of the camera 2, the MEMSmirror 10, and the cover glass 6 disposed therebetween. To opticallyremove artifacts and remove ghosting, the angle of incidence iscalibrated. The Brewster angle of the cover glass (i.e., the angle atwhich polarized light completely transmits through a transparentsurface) was determined to be 22.5 degrees from cover glass manufacturerspecifications, and by adjusting the mirror and camera angles to be nearthis value, partial cover reflection dampening is achieved. The coverglass transmits wavelengths between 675 nm and 1040 nm, and reflectswavelengths outside of this range. Working within these specifications,a notch filter is selected that transmits wavelengths between 800 nm and900 nm, and reflects wavelengths outside this range. Combined with an850 nm NIR light source to illuminate the scene, the reflection from thecover glass is further reduced. Further, an absorbing black paper may beinserted in the center of the virtual camera field of view to removeincident light that would be reflected by the cover glass, and the MEMSmirror is run outside of this region. Together, these elements removereflected artifacts and ghosting to produce a clear and unobstructedimage.

Performance Calibration

In various embodiments, directional control of the MEMS mirror's scanuses a learned model that changes the visual attention, based on a priorscan, to optimize performance on visual tasks (e.g., recognition,tracking, depth perception, etc.). As a result, the camera 2 can enablerealistically fast artificial saccades and allows artificial saccadicmovements to improve visual task accuracy. In particular, the directionof the MEMS mirrors at any time t is defined as (θ(t), ϕ(t)). A“control” camera is defined as a device that provides information Iabout the scene—noting that the “control” camera could be the proposeddevice, or could be a secondary camera (e.g., a secondary color camera,thermal camera, event camera, etc.). The control algorithm A takes asinput the information collected so far by the secondary camera(s) andoutputs a trajectory that the MEMS mirror can follow to optimize thevision task, A(I)=(θ(T), ϕ(T)), where T is a series of time instances.

The control algorithm A is jointly learned from data collectedpreviously, and also by using the calibration information from theoptical setup described above. This joint optimization using a learningalgorithm allows the camera to measure information needed to optimizeparticular tasks.

In various embodiments, a calibration algorithm specific to the class offoveated cameras is used to calibrate a fast foveation camera 2. Acalibration between the temporal sampling of the high speedcamera/imaging sensor (e.g., the camera frame-rate F) and the movementof the optical modulator (e.g., the mirror-speed L of the MEMS mirror inan example embodiment) is performed. FIG. 6 provides a flowchartillustrating various processes, procedures, operations, and/or the likeperformed to calibrate a fast foveation camera 2 and/or to learn acontrol algorithm A.

Starting at block 402, an image of a calibration scene is captured usinga fast foveation camera 2. For example, a fast foveation camera 2 may beused to image a calibration scene. In various embodiments, thecalibration scene is a highly textured scene. In an example embodiment,the highly textured scene is a plane comprising texture such thatcalibration may be performed without interference from visual effectscaused by the topology of the calibration scene. In an exampleembodiment, the texture is a topological or tactile texture (e.g.,ridges, bumps, indents, and/or other features that provide a tactileeffect). In an example embodiment, the texture if a visual texture(e.g., comprising gradients of color and/or one or more colors thatprovide a visual effect). For example, the image of the calibrationscene may be captured by the MEMs controller 20 causing the MEMs mirror10 to move in accordance with a control algorithm A such that a beam oflight reflected from the calibration scene is reflected by the MEMsmirror 10 to the high speed passive sensor 30. The high speed passivesensor 30 may capture the beam of light reflected from the calibrationscene such that an image of the calibration scene is captured. In anexample embodiment, the image is captured by the deep mapping processor40. For example, the high speed passive sensor 30 provides collectedimage information/data to the deep mapping processor 40 for processingthereof for the formation and/or capturing of an image (e.g., the imageof the calibration scene).

At block 404, the image of the calibration scene is processed and/oranalyzed to determine if a blur is present in the image. In variousembodiments, the deep mapping processor 40 processes and/or analyzes theimage of the calibration scene to determine if a blur is present in theimage. In an example embodiment, the deep mapping processor 40 iscommunication (e.g., wired or wireless communication) with an imageprocessing computing entity 60 and the image processing computing entity60 processes and/or analyzes the image of the calibration scene todetermine if a blur is present in the image. In various embodiments, anamount of blur may be determined (e.g., barely blurred, blurred to thepoint of smearing, and/or the like).

If, at block 404, it is determined (e.g., by the deep mapping processor40 and/or the image processing computing entity 60) that there is noblur present in the image of the calibration scene, the processcontinues to block 406. At block 406, the frame rate F of the fastfoveation camera 2 is reduced and the calibration scene is re-imaged.The frame rate F may be reduced until a small blur (e.g., a blur withoutsmearing) is present in the captured image of the calibration scene. Forexample, the deep mapping processor 40 and/or the image processingcomputing entity 60 may adjust or cause adjustment of the controlalgorithm A such that the frame rate F is reduced. The MEMs controller20 may then control the MEMs mirror 10 in accordance with the adjustedcontrol algorithm A such that the high speed passive sensor 30 providesdigital image information/data to the deep mapping processor 40 suchthat a new image of the calibration scene is captured. The process maythen be continued until a small blur (e.g., a blur without smearing) ispresent in the captured image of the calibration scene.

If, at block 404, a small blur (e.g., a blur without smearing) ispresent in the captured image of the calibration scene, the process iscomplete, and the control algorithm A is set. For example, if at block404, the deep mapping processor 40 and/or the image processing computingentity 60 determines that a small blur (e.g., a blur without smearing)is present in the image of the calibration scene, the calibrationprocess may be determined to be complete and the calibration process mayend.

If at block 404, a significant blur (e.g., a blur with smearing) ispresent in the captured image of the calibration scene, the processcontinues to block 408. At block 408, the mirror speed L of the fastfoveation camera 2 is reduced and the calibration scene is re-imaged.The mirror speed L may be reduced until a small blur (e.g., a blurwithout smearing) is present in the captured image of the calibrationscene. For example, the deep mapping processor 40 and/or the imageprocessing computing entity 60 may adjust or cause adjustment of thecontrol algorithm A such that the mirror speed L is reduced. The MEMscontroller 20 may then control the MEMs mirror 10 in accordance with theadjusted control algorithm A such that the high speed passive sensor 30provides digital image information/data to the deep mapping processor 40such that a new image of the calibration scene is captured. The processmay then be continued until a small blur (e.g., a blur without smearing)is present in the captured image of the calibration scene.

Controlling the MEMS Mirror Motion to Capture a Scene

Given the optically calibrated foveating camera as described above, itis desirable to move the MEMS mirror to best capture the intended scene.As shown in FIG. 2, the camera captures reflections off of the MEMSmirror, whose azimuth and elevation are given by changes in controlvoltages over time. (θ(V(t)),ϕ(V(t)) over the mirror field of view(ω_(mirror)).

The system bandwidth may be M pixels/second. Given an integer k>0, acamera is used that captures M/k pixel images at k images per second inthe foveating sensor. Since the mirror moves quickly, new active visioncontrol is possible to distribute the k instances of the viewing conewithin a second. The advantage of MEMS mirrors is the speed of motion,allowing the mirror to scan and quickly attend to a region-of-interest.

Consider a virtual plane perpendicular to the optical axis and parallelto the MEMS mirror in a resting, horizontal state, i.e. (θ=0,ϕ=0). Everyangular pose of the MEMS mirror (θ,ϕ) corresponds to a location (x, y)on given perspective scaling. Consider a scene with two targets.According to example embodiments described herein, the focus on targetsmay be the faces of two people. Long range eye tracking is possible ifthe mirror moves quickly between the two face locations. To do this, atight one-dimensional sinusoid of amplitude

$\frac{L_{r}}{2},$

bounded by the face locations, with one of the face locations being the“anchor” of the system, (x_(r), y_(r)), while its orientation given bythe angle ∝_(r) with respect to an arbitrary reference vector, such asone parallel to the lower edge of the MEMS mirror. The state of thesensor may be denoted as the triplet: q_(r)=(x_(r),y_(r),α_(r)), andthis state exists in a space of possible configurations given by thesensor hardware limits for one dimensional motion:∪=(L_(min),L_(max))×(ω_(min),Ω_(max)). The problem of control requires asolution that changes the state q_(r) of the sensor to enable targetimaging.

To change the state to match the people's motion around the scene, acontrol vector u_(r)=(v_(r),Ω_(r)) may be defined for a a new desiredmotion by specifying the velocity v_(r) by which the length of theone-dimensional motion should change and the angular velocity ω_(r) bywhich the angle of the one-dimensional motion should change. An optionalKalman filter may be used to estimate the current state of the MEMSmirror's one-dimensional motion and the face locations, given a previousstate and face locations of the desired control vector.

Optional Kalman Filter for State and Target Tracking

A probability distribution of the targets over time is necessary tocontrol the viewing direction of the MEMS mirror in the camera.Embodiments described herein use a vision-based face-tracker as a proxyfor the filter to estimate the distribution. However, the Kalman filtertracker may be used. The Kalman filter tracker defines a control matrixthat uses the desired control vector and estimates the shift that itinduces in the current state of the sensor. If the index is set for timeask, and if an instantaneous time is denoted at δt, then the controlmatrix B_(r)(k) for the state sensor is:

${B_{r}(k)} = {\begin{bmatrix}{\cos \; \alpha_{r}\delta t} & 0 \\{\sin \; \alpha_{r}\delta t} & 0 \\0 & {\delta t}\end{bmatrix}u_{r}}$

Where infinitesimal shifts in time result in changes in the length andangle of the MEMS mirror's one-dimensional motion. A prediction for theMEMS mirror state that depends upon the control vector u_(r)(k):

q _(r)(k+1)=I ₃ q _(r) +B _(r)(k)u _(r)(k)+Q _(r)

Where Q_(r) is the covariance matrices of the MEMS controller and I₃ isthe identity representing the state transition for a calibrated,controlled sensor (e.g., only the control vector and noise matters inchanging the state)

To complete the full predict step, estimates are added to the targets.In tracking two people, their 3D face locations (e.g., the center oftheir respective faces) project onto the virtual plane Π at two 2Dlocations, given by q_(f)=[x_(lf) y_(lf) x_(rf) y_(rf)] for the leftface and right face.

The full state vector as containing all MEMS mirror information plus theface locations is denoted as: q(k)=[q_(r) ^(T)(k) q_(f) ^(T)(k)]^(T).Since there is no control over location of the faces, the full controlvector u(k)=[u_(r)(k)0]^(T).

For the target faces, the state transition vector is given calculatingthe optical flow of the left face [f_(xlf) f_(ylf)] and the right face[f_(xrf) f_(yrf)]. To cast optimal flow within the linear Kalman flowequations, a simple trick is employed to convert the 2D vector additionof optical flow to multiplication by setting

${{{of_{1}} = \frac{x_{lf} + f_{xlf}}{x_{lf}}},{{of}_{2} = \frac{y_{lf} + f_{ylf}}{y_{lf}}},{{of}_{3} = \frac{x_{rf} + f_{xrf}}{x_{rf}}},{and}}\mspace{20mu}$${of}_{4} = {\frac{y_{rf} + f_{yrf}}{y_{rf}}.}$

This enables the specification of a transition matrix:

${F = \begin{pmatrix}I_{3} & 0 \\0 & {OF}\end{pmatrix}},{where}$ ${OF} = \begin{pmatrix}{of_{1}} & 0 & 0 & 0 \\0 & {of_{2}} & 0 & 0 \\0 & 0 & {of_{3}} & 0 \\0 & 0 & 0 & {of_{4}}\end{pmatrix}$

Augmenting B(k)=[B_(r) (k); 0], with the full predict equation:

q(k+1)=Fq(k)+B(k)u(k)+ω

Where ω represents the process noise in the MEMS controller and thetarget motion is denoted by matrices Q_(r) and Q_(t). Let the covariancematrix of the state vector (MEMS mirror+target faces) be P_(k)=[P_(r)(k)0; 0 P_(t)(k)] where P_(r)(k) is the covariance matrix representing theuncertainty in the MEMS mirror state and P_(t) (k) is the covariancematrix representing uncertainty in the target location. Then the changein uncertainty is:

P(k+1)=[B _(r)(k)^(T) P _(r) B _(r)(k)0;0P _(t)]+[Q _(r)(k)0;0Q _(t)(k)]

Where the untracked noise is represented by the covariance terms of thenoise in the MEMS controller and the target Q_(r) and Q_(t).

The update step for the entire system is given by two types of sensormeasurements. The first is the proprioceptive sensor based on voltagemeasurements made directly with a universal serial bus (USB)oscilloscope that receives the same voltages sent to the MEMS. Thesecond is a camera that views the reflections of the mirror and appliesa standard face recognition classifier to each location, determining aprobability distribution of left and right face locations across theFOV. From these two measurements both estimated state vector and itscovariance can be proposed as [z(k), R(k)]. Note that the measurementfunction (usually denoted as H(k)) is the identity in this configurationas all the probability distributions share the same domain (e.g., the 2Dplane Π created in front of the sensor. The remaining Kalman filterequations are:

K′=P(k+1)(P(k+1)+R(k+1))⁻¹

q′(k+1)=q(k+1)+K′(z(k+1)−q(k+1))

P′(k+1)=P(k+1)−K′P(k+1)

Metrics for Mirror Control

A metric for mirror control may be the difference between the groundtruth (unknown) state q(k) and the current state as predicted by thefilter q(k+1). However, if there is no face detection, the filter cannotbe applied, and the previous state is defaulted to moved by controlvector given by q(k+1). With P_(d), the probability that all faces weredetected successfully.

M _(k) =P _(d) E[e′(k+1)^(T) e′(k+1)]+(1−P _(d))E[e(k+1)^(T) e(k+1)]

where: e′(k+1)=q(k)−q′(k+1) and e(k+1)=q(k)−q(k+1).

Using the trace trick, M_(k) can be converted into an expression usingthe covariance matrices:

M _(k) =tr[P(k+1)]−P _(d)(tr[P(k+1)]−tr[P′(k+1)])

Since tr[P(k+1)]−tr[P′(k+1)] is always positive, due to uncertaintyreduction of a Kalman filter, maximizing P_(d) reduces the error M_(k).This metric for good performance illuminates how to control the MEMSmirror with u_(r).

Updating the Control Vector

The conclusion of the previous section is depicted as a control law:max_(u) _(r) P_(d), where P_(d) is defined as the probability that allof the faces are detected, and is given by integrating the probabilityof seeing a face over the MEMS mirror path given by the state of thesensor q_(r) (k)=(x_(r)(k), y_(r)(k),α_(r)(k)). A gradient-basediterative update to the control vector is now used given the sensorstate and uncertainty. Calculating P_(d) as a slice given a parameter s,locations along which the probability P_(d) must be integrated can beexpressed as:

P _(d)(q _(r)(k))=∫_(s=0) ^(L) f _(t)(x _(r)(k)+s cos α_(r)(k),y_(r)(k)+s sin α_(r)(k))ds

Where f_(t) is the probability distribution function of the faces of thecanonical plane Π. The distribution f_(t) comes from the estimates offace location, which could be from the Kalman filter described above orfrom another process, and can be modeled as an air of bi-variateGaussian distributions, of equal weight (e.g., the mixing parameter is0.5), such that f_(t)(x, y)=f_(l)(x, y)+f_(r)(x, y), where each Gaussiancomponent centered at two previously estimated left and right facelocations given by q_(f)(k−1)=[x_(lf)(k−1) y_(lf)(k−1) x_(rf)(k−1)y_(rf)(k−1)]. Said differently, P_(d) is an integral along a slicethrough two bivariate Gaussian distributions. For each left and rightcase, the correlation matrix is known of both 2D Gaussians, from theKalman filter, given by [σ_(1l)σ_(2l)ρ_(l)] for the left and[σ_(1r)σ_(2r)ρ_(r)] for the right. Thus, the term f_(t)(x_(r)(k)+s cosα_(r)(k), y_(r)(k)+s sin α_(r)(k)) can be split into two components,where x=x_(r)(k)+s cos α_(r) (k) and y=y_(r)(k)+s sin α_(r) (k), thefirst given by f_(l)(x, y):

$\frac{1}{2\pi \; \sigma_{1l}\sigma_{2l}\sqrt{1 - \rho_{l}^{2}}}e\; 2^{\frac{\frac{{({x - x_{lf}})}^{2}}{\sigma_{lf}^{2}} - \frac{2\; \rho \; {l{({x - x_{lf}})}}{({y - y_{lf}})}}{\sigma_{1l}\sigma_{2l}} + \frac{{({y - y_{lf}})}^{2}}{\sigma_{2l}^{2}}}{2{({1 - \rho_{l}^{2}})}}}$

And the second given by f_(r)(x, y):

$\frac{1}{2\pi \; \sigma_{1r}\sigma_{2r}\sqrt{1 - \rho_{r}^{2}}}e\; 2^{\frac{\frac{{({x - x_{rf}})}^{2}}{\sigma_{rf}^{2}} - \frac{2\; \rho \; {r{({x - x_{rf}})}}{({y - y_{rf}})}}{\sigma_{1r}\sigma_{2r}} + \frac{{({y - y_{rf}})}^{2}}{\sigma_{2r}^{2}}}{2{({1 - \rho_{r}^{2}})}}}$

Gradient Descent

Maximizing the value of P_(d) can be tackled with gradient descent.First, it is established that P_(d) has at most two global maxima bylinking it to the well-known Radon transform. Next, it is shown thatthis formulation of P_(d) is bounded. For Global maxima: P_(d) isobtained by slicing through the two Gaussians at a line segment given byq_(r)=(x_(r),y_(r),α_(r)). By reconstituting this as a slice through aline with y intercept y_(rad)=y_(r)+x_(r)*(tan(α_(r)) and slopes_(rad)=tan(α_(r)), it is identified that P_(d) is the Radontransformation of a bi-variate distribution. For each Gaussiandistribution individually, this transform has been shown to be unimodalwith a global maxima and continuous for a zero-mean Gaussian. Astranslations and affine transformations do not affect the radontransform, these hold for any Gaussian distribution. For the sum ofRadon transforms of two such Gaussians, there can be at most two globalmaxima (if these are equal) and at least one maxima (if these overlapperfectly). Further, since the sum of two continuous functions is alsocontinuous, the Radon transform of the bi-variate distribution is alsocontinuous. Finally, the Radon transform is computationally burdensomefor a robot to compute at every frame, which supports using iterativegradient descent.

For Bounded domain: any slice through the bi-variate distribution isconsidered. For example, a slice that has the centers of the twoGaussians on the same side of the slice. By moving the slice toward thetwo centers, both components of P_(d) can be increased exponentially andmonotonically. So such a slice cannot maximize P_(d). From the above,the slice that maximizes P_(d) goes through a line segment between thecenters of the two Gaussians. Note that the optimal slice must intersectthis line segment somewhere. Said differently, the domain within theRadon transform of the bi-variate Gaussians, where the maximal slice issought, is bounded.

For Optimal path not the line joining Gaussians' center: While the linejoining the Gaussians' center is a useful heuristic, it is not a generalsolution since the length of the integral L could be smaller than thedistance between the Gaussian centers. Secondly, the heuristic tends towork when the Gaussians are similar. If one Gaussian dominates, theoptimal line can be different. From these arguments of bounded domainand continuity, the application of gradient descent is a reasonablestrategy for lightweight optimization of control law.

For Gradient descent, the Jacobian (i.e. derivatives) ofP_(d)(q_(r)(k+1)) given by u_(r) are computed:

${\frac{\delta {P_{d}\left( {q_{r}\left( {k + 1} \right)} \right)}}{\delta u_{r}} = \frac{\delta {P_{d}\left( {q_{r}\left( {k + 1} \right)} \right.}}{\delta {q_{r}\left( {k + 1} \right)}}}\frac{\delta \; {q_{r}\left( {k + 1} \right)}}{\delta \; u_{r}}$

Since the second term is the sensor motion model, B_(r)(k)δt, the firstterm needs to be calculated:

$\frac{\delta {P_{d}\left( {q_{r}\left( {k + 1} \right)} \right)}}{\delta {u_{r}\left( {k + 1} \right)}} = \begin{bmatrix}{\frac{\delta}{\delta \; x_{r}}{P_{d}\left( {q_{r}\left( {k + 1} \right)} \right)}} \\{\frac{\delta}{\delta \; y_{r}}{P_{d}\left( {q_{r}\left( {k + 1} \right)} \right)}} \\{\frac{\delta}{\delta \; \alpha_{r}}{P_{d}\left( {q_{r}\left( {k + 1} \right)} \right)}}\end{bmatrix}$

Setting x=x_(r)(k)+s cos α_(r)(k) and y=y_(r)(k)+s sin α_(r)(k), and bysplitting f_(t) into left and right Gaussians, the above can berewritten as:

$\frac{\delta {P_{d}\left( {q_{r}\left( {k + 1} \right)} \right)}}{\delta {u_{r}\left( {k + 1} \right)}} = {\begin{bmatrix}{\frac{\delta}{\delta \; x_{r}}{\int_{s = 0}^{L}{{f_{l}\left( {x,y} \right)}ds}}} \\{\frac{\delta}{\delta \; y_{r}}{\int_{s = 0}^{L}{{f_{l}\left( {x,y} \right)}ds}}} \\\left. {\frac{\delta}{\delta \; \alpha_{r}}{\int_{s = 0}^{L}{{f_{l}\left( {x,y} \right)}ds}}} \right)\end{bmatrix} + \begin{bmatrix}{\frac{\delta}{\delta \; x_{r}}{\int_{s = 0}^{L}{{f_{r}\left( {x,y} \right)}ds}}} \\{\frac{\delta}{\delta \; y_{r}}{\int_{s = 0}^{L}{{f_{r}\left( {x,y} \right)}ds}}} \\{\frac{\delta}{\delta \; \alpha_{r}}{\int_{s = 0}^{L}{{f_{r}\left( {x,y} \right)}ds}}}\end{bmatrix}}$

These gradients can be calculated after every iteration of the Kalmanfilter, allowing for the closed form update of the MEMS mirror based onthe movement of the faces, sensor state, and uncertainty.

Remote Eye-Tracking for Frontal Faces

Remote eye-tracking for frontal faces has potential applications insituations where the faces are directly viewed by the camera, such asfor human-robot interaction, automobile safety, smart homes, and ineducational/classroom settings. Remote eye-tracking using the disclosedfoveating camera provides improvements over conventional methods throughuse of the MEMS mirror enabled foveating camera to capture images andusing a convolutional neural network to analyze the images. Embodimentsare able to track the gaze of multiple people at greater distance thanconventional eye-tracking methods.

Exemplary Post-Processing

In various embodiments, one or more post-capture algorithms specific tothe class of foveated cameras are used to perform post-processing ofimages captured by a fast foveation camera 2. In various embodiments, apost-processing method is used to remove imaging artifacts from theimage(s) captured by the fast foveation camera 2 and improve thefidelity of the imaging camera, in various embodiments. In particular,the motion of the MEMS mirror in resonance is not equiangular. Thisresults in non-uniform sampling and different noise characteristics fordifferent directions of movement of the mirror. Our method follows fromthe calibration, and since we know the direction of the MEMS mirror foreach measurement of the camera, we can project these onto the hemisphereof directions (i.e. an environment map centered at the camera) andperform angular-based image processing to allow for super resolution anddenoising.

Exemplary Composite Camera

FIGS. 7 and 8 illustrate two example embodiments of a composite camera5, 5′ comprising a fast foveation camera 2. Various embodiments ofcomposite camera may comprise multiple fast foveation cameras 2. Invarious embodiments, the composite camera 5, 5′ further comprises asecondary camera 50. In various embodiments, the secondary camera 50 maybe a fast foveation camera 2, a digital (optical) camera (e.g., a colorcamera), thermal (e.g., infrared) camera, event camera, ultra-violetcamera, and/or other camera. In various embodiments, the secondarycamera 50 is configured, oriented, aligned, and/or the like such thatthe field of view of the secondary camera 50 at least partially overlapswith the field of view a fast foveation camera 2 of the composite camera5, 5′. In an example embodiment, the secondary camera 50 is in directcommunication with the fast foveation camera 2 (e.g., the deep mappingprocessor 40) via a wired or wireless connection. In an exampleembodiment, the secondary camera 50 is in directed communication with animage processing computing entity 60 and in indirect communication witha fast foveation camera 2 (e.g., via the image processing computingentity 60). For example, an example embodiment of a composite camera 5′comprises a secondary camera 50 in communication with an imageprocessing computing entity 60 that is in communication with a fastfoveation camera 2 (e.g., deep mapping processor 40). In variousembodiments, the secondary camera 50 is configured to capture a firstimage of a scene (e.g., an optical image, thermal image, UV image,and/or the like). The image processing computing entity 60 and/or deepmapping processor 40 is configured to receive the first image of thescene captured by the secondary camera 50 and process and/or analyze thefirst image of the scene to identify one or more areas of interestwithin the scene. The deep mapping processor 40 may then cause the MEMscontroller 20 to control the MEMs mirror 10 such that one or more secondimages are captured. In various embodiments, the one or more secondimages are each an image of an area of interest within the scene. Invarious embodiments, the one or more second images are of higherresolution (e.g., higher angular resolution) than the first image.

Exemplary Use of a Composite Camera

FIG. 9 provides a flowchart illustrating various processes, procedures,operations, and/or the like for imaging a scene using a composite camera5, 5′. Starting at block 602, a first image of a scene is captured withthe secondary camera 50. For example, a secondary camera 50 captures afirst image of a scene.

At block 604, the first image of the scene is processed and/or analyzedto identify one or more areas of interest within the scene. For example,the secondary camera 50 may provide the first image such that an imageprocessing computing entity 60 and/or deep mapping processor 40 receivesthe first image and processes and/or analyzes the first image toidentify one or more areas of interest within the scene. In an exampleembodiment, the one or more areas of interest within the scene are eachwithin a field of view of at least one fast foveation camera 2 of thecomposite camera 5, 5′.

At block 606, at least one second image is captured. Each second imageis an image of an area of interest identified within the scene. In anexample embodiment, the resolution of a second image is higher than theresolution of the first image. In various embodiments, a fast foveationcamera 2 of the composite camera 5, 5′ is used to capture a secondimage. For example, the deep mapping processor 40 may cause the MEMscontroller 20 to control the MEMs mirror 10 such that the high speedpassive sensor 30 captures digital image information/data that isprovided to the deep mapping processor 40 such that the deep mappingprocessor 40 captures a second image of an area of interest within thescene.

At block 608, the at least one second image is associated with the firstimage and stored and/or provided. For example, the deep mappingprocessor 40 may associate the at least one second image with the firstimage and provide and/or store the first and second images. For example,the deep mapping processor 40 may provide the at least one second imagesuch that an image processing computing entity 60 receives the secondimage, associates the second image with the first image, and stores(e.g., in memory 210, 215, see FIG. 10) and/or provides the first andsecond images (e.g., via communications interface 220 or a userinterface).

Exemplary Image Processing Computing Entity

In various embodiments, one or more image processing computing entities60 may be used to perform one or more calibration steps for calibratinga fast foveation camera 2, learning a control algorithm A, performingpost-processing of an image captured by a fast foveation camera 2,processing an image captured by a secondary camera 50 of a compositecamera, and/or the like. FIG. 10 provides a schematic of an imageprocessing computing entity 60 according to one embodiment of thepresent invention. In general, the terms computing entity, entity,device, system, and/or similar words used herein interchangeably mayrefer to, for example, one or more computers, computing entities,desktop computers, mobile computing entities, laptops, distributedsystems, servers or server networks, processing devices, processingentities, the like, and/or any combination of devices or entitiesadapted to perform the functions, operations, and/or processes describedherein. Such functions, operations, and/or processes may include, forexample, transmitting, receiving, operating on, processing, displaying,storing, determining, creating/generating, monitoring, evaluating,comparing, and/or similar terms used herein interchangeably. In oneembodiment, these functions, operations, and/or processes can beperformed on data, content, information, and/or similar terms usedherein interchangeably.

As indicated, in one embodiment, the image processing computing entity60 includes one or more communications interfaces 220 for communicatingwith various computing entities, such as by communicating data, content,information, and/or similar terms used herein interchangeably that canbe transmitted, received, operated on, processed, displayed, stored,and/or the like. For instance, the image processing computing entity 60may communicate with secondary camera 50, deep mapping processor 40,and/or the like.

As shown in FIG. 7, in one embodiment, the image processing computingentity 60 includes or is in communication with one or more processingelements 205 (also referred to as processors, processing circuitry,and/or similar terms used herein interchangeably) that communicate withother elements within the image processing computing entity 60 via abus, for example. As will be understood, the processing element 205 maybe embodied in a number of different ways. For example, the processingelement 205 may be embodied as one or more complex programmable logicdevices (CPLDs), microprocessors, multi-core processors, coprocessingentities, application-specific instruction-set processors (ASIPs),graphics processors, and/or controllers. Further, the processing element205 may be embodied as one or more other processing devices orcircuitry. The term circuitry may refer to an entirely hardwareembodiment or a combination of hardware and computer program products.Thus, the processing element 205 may be embodied as integrated circuits,application specific integrated circuits (ASICs), field programmablegate arrays (FPGAs), programmable logic arrays (PLAs), hardwareaccelerators, other circuitry, and/or the like. As will therefore beunderstood, the processing element 205 may be configured for aparticular use or configured to execute instructions stored in volatileor non-volatile media or otherwise accessible to the processing element205. As such, whether configured by hardware or computer programproducts, or by a combination thereof, the processing element 205 may becapable of performing steps or operations according to embodiments ofthe present invention when configured accordingly.

In one embodiment, the image processing computing entity 60 may furtherinclude or be in communication with non-volatile media (also referred toas non-volatile storage, memory, memory storage, memory circuitry and/orsimilar terms used herein interchangeably). In one embodiment, thenon-volatile storage or memory may include one or more non-volatilestorage or memory media 210 as described above, such as hard disks, ROM,PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards, Memory Sticks,CBRAM, PRAM, FeRAM, RRAM, SONOS, racetrack memory, and/or the like. Aswill be recognized, the non-volatile storage or memory media may storedatabases, database instances, database management system entities,data, applications, programs, program modules, scripts, source code,object code, byte code, compiled code, interpreted code, machine code,executable instructions, and/or the like. The term database, databaseinstance, database management system entity, and/or similar terms usedherein interchangeably may refer to a structured collection of recordsor information/data that is stored in a computer-readable storagemedium, such as via a relational database, hierarchical database, and/ornetwork database.

In one embodiment, the image processing computing entity 60 may furtherinclude or be in communication with volatile media (also referred to asvolatile storage, memory, memory storage, memory circuitry and/orsimilar terms used herein interchangeably). In one embodiment, thevolatile storage or memory may also include one or more volatile storageor memory media 215 as described above, such as RAM, DRAM, SRAM, FPMDRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM, RDRAM, RIMM,DIMM, SIMM, VRAM, cache memory, register memory, and/or the like. Aswill be recognized, the volatile storage or memory media may be used tostore at least portions of the databases, database instances, databasemanagement system entities, data, applications, programs, programmodules, scripts, source code, object code, byte code, compiled code,interpreted code, machine code, executable instructions, and/or the likebeing executed by, for example, the processing element 205. Thus, thedatabases, database instances, database management system entities,data, applications, programs, program modules, scripts, source code,object code, byte code, compiled code, interpreted code, machine code,executable instructions, and/or the like may be used to control certainaspects of the operation of the image processing computing entity 60with the assistance of the processing element 205 and operating system.

As indicated, in one embodiment, the image processing computing entity60 may also include one or more communications interfaces 220 forcommunicating with various computing entities, such as by communicatingdata, content, information, and/or similar terms used hereininterchangeably that can be transmitted, received, operated on,processed, displayed, stored, and/or the like. Such communication may beexecuted using a wired data transmission protocol, such as fiberdistributed data interface (FDDI), digital subscriber line (DSL),Ethernet, asynchronous transfer mode (ATM), frame relay, data over cableservice interface specification (DOCSIS), or any other wiredtransmission protocol. Similarly, the image processing computing entity60 may be configured to communicate via wireless external communicationnetworks using any of a variety of protocols, such as general packetradio service (GPRS), Universal Mobile Telecommunications System (UMTS),Code Division Multiple Access 2000 (CDMA2000), CDMA2000 1× (1×RTT),Wideband Code Division Multiple Access (WCDMA), Global System for MobileCommunications (GSM), Enhanced Data rates for GSM Evolution (EDGE), TimeDivision-Synchronous Code Division Multiple Access (TD-SCDMA), Long TermEvolution (LTE), Evolved Universal Terrestrial Radio Access Network(E-UTRAN), Evolution-Data Optimized (EVDO), High Speed Packet Access(HSPA), High-Speed Downlink Packet Access (HSDPA), IEEE 802.11 (Wi-Fi),Wi-Fi Direct, 802.16 (WiMAX), ultra-wideband (UWB), infrared (IR)protocols, near field communication (NFC) protocols, Wibree, Bluetoothprotocols, wireless universal serial bus (USB) protocols, and/or anyother wireless protocol. The image processing computing entity 60 mayuse such protocols and standards to communicate using Border GatewayProtocol (BGP), Dynamic Host Configuration Protocol (DHCP), Domain NameSystem (DNS), File Transfer Protocol (FTP), Hypertext Transfer Protocol(HTTP), HTTP over TLS/SSL/Secure, Internet Message Access Protocol(IMAP), Network Time Protocol (NTP), Simple Mail Transfer Protocol(SMTP), Telnet, Transport Layer Security (TLS), Secure Sockets Layer(SSL), Internet Protocol (IP), Transmission Control Protocol (TCP), UserDatagram Protocol (UDP), Datagram Congestion Control Protocol (DCCP),Stream Control Transmission Protocol (SCTP), Hypertext Markup Language(HTML), and/or the like.

As will be appreciated, one or more of the image processing computingentity's 60 components may be located remotely from other imageprocessing computing entity 60 components, such as in a distributedsystem. Furthermore, one or more of the components may be combined andadditional components performing functions described herein may beincluded in the image processing computing entity 60. Thus, the imageprocessing computing entity 60 can be adapted to accommodate a varietyof needs and circumstances.

In an example embodiment, the image processing computing entity 60further comprises and/or is in communication with one or more userinterface devices. For example, the user interface devices comprise userinput devices and/or user output devices, in various embodiments. Someexamples of user input devices include a touch screen, soft or hardkeyboard, mouse, touchpad, microphone, and/or the like. Some examples ofuser output devices include a display, monitor, speaker, 2D or 3Dprinter, and/or the like. In various embodiments, the image processingcomputing entity 60 is configured to provide an interactive userinterface via the one or more user interface devices.

In example embodiments, the image processing computing entity 60 may bein communication with one or more other secondary cameras 50, deepmapping processors 40, and/or other computing entities.

CONCLUSION

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

1. A camera comprising: a passive sensor; a dynamic optical modulator;and an optical modulator controller configured to control theorientation of the dynamic optical modulator, wherein the dynamicoptical modulator is configured to reflect a beam of light from scene ofinterest onto the passive sensor.
 2. The camera of claim 1, wherein thepassive sensor is a high speed passive sensor.
 3. The camera of claim 1,wherein the passive sensor is a CCD or CMOS image sensor.
 4. The cameraof claim 1, wherein the dynamic optical modulator is a scanning mirroror an array of scanning mirrors.
 5. The camera of claim 1, wherein thedynamic optical modulator is a MEMs mirror or an array of MEMs mirrors.6. The camera of claim 5, wherein each MEMs mirror comprises a pluralityof microactuators.
 7. The camera of claim 6, wherein a centralreflective mirror plate of each MEMs mirror is suspended by fourmicroactuators.
 8. The camera of claim 7, wherein each of themicroactuators are configured to be electrically controlled to set theheight of a corresponding edge of the central reflective mirror plate,the microactuator being anchored to the corresponding edge.
 9. Thecamera of claim 6, wherein each of the microactuators is made of afolded thin-film beam.
 10. The camera of claim 6, wherein the opticalmodulator controller is configured to control each of themicroactuators.
 11. The camera of claim 1, further comprising aprocessor configured to receive digital image data from the passivesensor and to control the optical modulator controller.
 12. The cameraof claim 11, wherein the processor is configured to control the opticalmodulator controller in accordance with a control algorithm.
 13. Thecamera of claim 1, wherein the control algorithm provides a mirror speedfor movement of the optical modulator and a frame-rate speed forcapturing the digital image data by the passive sensor.
 14. The cameraof claim 12, further comprising a cover glass positioned between thedynamic optical modulator and the passive sensor, wherein the passivesensor and the dynamic optical modulator are positioned at anglesrelative to the cover glass established to dampen reflections from thecover glass.
 15. A composite camera comprising: a secondary camera, aprimary camera, wherein the primary camera is the primary camera ofclaim
 1. 16. The composite camera of claim 15, wherein the secondarycamera is one of an optical camera, a thermal camera, or an ultra-violetcamera.
 17. The composite camera of claim 15, wherein a field of view ofthe secondary camera and a field of view of the primary camera overlapat least in part.
 18. The composite camera of claim 15, wherein thesecondary camera is configured to capture a first image of a scene andthe primary camera is configured to capture one or more second images,each of the second images being of an area of interest within the scene.19. The composite camera of claim 18, wherein each second image has ahigher resolution than the first image.
 20. The composite camera ofclaim 18, configured to associate with one or more second images withthe first image and provide and/or store the first and second images.21. A method of imaging a scene with a composite camera, the compositecamera being the composite camera of claim 15, the method comprising:capturing a first image of a scene with the secondary camera; analyzingthe first image with a processor to identify one or more areas ofinterest within the scene; causing the optical modulator to control thedynamic optical modulator such that the primary camera captures one ormore second images, each second image being an image of one of the oneor more areas of interest within the scene; associating the first andsecond images; and storing and/or providing the first and second images.22. The method of claim 21, wherein each second image has a higherresolution than the associated first image.
 23. The method of claim 21,further comprising performing post-processing on at least one secondimage to remove imaging artifacts from the at least one second image.24. A method for calibrating a control algorithm, the method comprising:(a) capturing an image of a calibration scene using a camera of claim 1;(b) analyzing the time of the calibration scene to determine if a bluris present in the image; (c) (i) responsive to determining that no bluris present in the image, reducing the frame rate of the camera andreturning to step (a); (ii) responsive to determining that a smearingblur is present in the image, reducing the mirror speed of the dynamicoptical modulator and returning to step (a); (iii) responsive todetermining that a non-smearing blur is present in the image,determining that the control algorithm is calibrated.
 25. The method ofclaim 24, wherein the calibration scene is a highly textured scene. 26.The method of claim 25, wherein the calibration scene is a highlytexture plane.
 27. The method of claim 25, wherein the highly texturedscene is textured with at least one of a tactile texture or a visualtexture.